High dimensionality has been a major problem for gene array-based cancer classification. Feature Selection (FS) is ordinarily used as a useful technique in order to reduce the dimension of the dataset. For that to get advantages from both methods of feature selection, Individual Feature Ranking (IFR) and Feature Subset Selection (FSS) are combined which is a hybrid approach. Information Gain fr...

Combining ant colony optimization (ACO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), this paper proposes a multiobjective evolutionary algorithm, MOEA/D-ACO. Following other MOEA/D-like algorithms, MOEA/D-ACO decomposes a multiobjective optimization problem into a number of single objective optimization problems. Each ant (i.e. agent) is responsible for solving one...

Ant colony optimization (ACO) algorithm is a new heuristic algorithm which has been demonstrated a successful technology and applied to solving complex optimization problems. But the ACO exists the low solving precision and premature convergence problem, particle swarm optimization (PSO) algorithm is introduced to improve performance of the ACO algorithm. A novel hybrid optimization (HPSACO) al...

This research work handles Software project scheduling problem with a hybrid approach. In this, a hybrid approach can handle combination of two different algorithms they are Event Based Scheduler(EBS) and Ant Colony Optimization(ACO). Which provide scheduling and also allocate the resources based on the project. The basic idea of the EBS is to adjust the allocation of employees at events and ke...

In this paper an ant colony optimization (ACO) algorithm for the minimum connected dominating set problem (MCDSP) is presented. The MCDSP become increasingly important in recent years due to its applicability to the mobile ad hoc networks (MANETs) and sensor grids. We have implemented a one-step ACO algorithm based on a known simple greedy algorithm that has a significant drawback of being easi...

This inquiry used ultrasonic waves to uptake paracetamol (PA) by using oak-based activated carbon (ACO). The surface of ACO was explored based on FT-IR, SEM, and XRD before after the adsorption. kinetic data for PA adsorption onto corresponds a pseudo-second-order model. Isothermal models Langmuir, Freundlich, D-R, Temkin were used. found be monolayer with 96.03% uptake, which Langmuir. thermod...

Background: This paper presents parametric modelling of flexible plate structures using ant colony optimization (ACO). The global optimization technique of ACO is utilized to obtain a dynamic model of a flexible plate structure based on one-step-ahead (OSA) prediction. Objective: The structure is subjected to three different disturbance signal types, namely random, pseudo random binary sequence...

As a powerful formalism, Bayesian networks play an increasingly important role in the Uncertainty Field. This paper proposes a hybrid method to discover the knowledge represented in Bayesian networks. The hybridmethod combines dependency analysis, ant colony optimization (ACO), and the simulated annealing strategy. Firstly, the new method uses order-0 independence tests with a self-adjusting th...

The purpose of this paper is to discuss the addition of a new operator, called an ACO operator, to a genetic algorithm. The operator is based on an analogy with Ant Colony Optimization. We use the ACO operator in an application of genetic algorithms to engineering design of conduit systems. The conduit optimization problem involves optimizing both the location of components of conduit systems a...

Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper proposes a new algorithm called ACO-E, to learn the structure of a Bayesian network. It does this by conducting a search through the space of equivalence classes of Bayesian networks using Ant Colony Optimization (ACO). To this end, two novel extensions of traditional ACO techniques are proposed and imp...